Overview

Dataset statistics

Number of variables12
Number of observations800
Missing cells0
Missing cells (%)0.0%
Duplicate rows88
Duplicate rows (%)11.0%
Total size in memory70.6 KiB
Average record size in memory90.3 B

Variable types

Categorical2
Numeric10

Alerts

Dataset has 88 (11.0%) duplicate rowsDuplicates
BMI is highly overall correlated with CLASSHigh correlation
CLASS is highly overall correlated with BMI and 1 other fieldsHigh correlation
Cr is highly overall correlated with UreaHigh correlation
HbA1c is highly overall correlated with CLASSHigh correlation
TG is highly overall correlated with VLDLHigh correlation
Urea is highly overall correlated with CrHigh correlation
VLDL is highly overall correlated with TGHigh correlation
CLASS is highly imbalanced (60.3%)Imbalance

Reproduction

Analysis started2024-03-14 06:11:48.676052
Analysis finished2024-03-14 06:12:20.629135
Duration31.95 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
M
455 
F
345 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 455
56.9%
F 345
43.1%

Length

2024-03-14T06:12:20.858357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T06:12:21.151789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 455
56.9%
f 345
43.1%

Most occurring characters

ValueCountFrequency (%)
M 455
56.9%
F 345
43.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 800
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 455
56.9%
F 345
43.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 455
56.9%
F 345
43.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 455
56.9%
F 345
43.1%

AGE
Real number (ℝ)

Distinct49
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.88
Minimum20
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-03-14T06:12:21.459878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile33.95
Q151
median55
Q359
95-th percentile65
Maximum79
Range59
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.3645416
Coefficient of variation (CV)0.15524391
Kurtosis2.0818179
Mean53.88
Median Absolute Deviation (MAD)4
Skewness-0.92601263
Sum43104
Variance69.965557
MonotonicityNot monotonic
2024-03-14T06:12:21.747749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
55 157
19.6%
60 73
 
9.1%
54 64
 
8.0%
51 46
 
5.8%
61 41
 
5.1%
56 40
 
5.0%
52 40
 
5.0%
50 32
 
4.0%
57 27
 
3.4%
58 26
 
3.2%
Other values (39) 254
31.8%
ValueCountFrequency (%)
20 1
 
0.1%
26 2
 
0.2%
28 2
 
0.2%
30 13
1.6%
31 8
1.0%
32 1
 
0.1%
33 13
1.6%
34 4
 
0.5%
35 5
 
0.6%
36 3
 
0.4%
ValueCountFrequency (%)
79 1
 
0.1%
77 2
 
0.2%
76 3
0.4%
75 2
 
0.2%
73 7
0.9%
71 1
 
0.1%
70 1
 
0.1%
69 4
0.5%
68 6
0.8%
67 5
0.6%

Urea
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0645537
Minimum0.5
Maximum38.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-03-14T06:12:22.072397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile2.3
Q13.6
median4.5
Q35.7
95-th percentile9.6
Maximum38.9
Range38.4
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation2.892686
Coefficient of variation (CV)0.57116305
Kurtosis35.914415
Mean5.0645537
Median Absolute Deviation (MAD)1
Skewness4.6172422
Sum4051.643
Variance8.3676321
MonotonicityNot monotonic
2024-03-14T06:12:22.431194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 31
 
3.9%
4 29
 
3.6%
3 28
 
3.5%
5 28
 
3.5%
4.8 28
 
3.5%
4.1 25
 
3.1%
4.5 21
 
2.6%
4.6 21
 
2.6%
3.8 20
 
2.5%
5.7 20
 
2.5%
Other values (95) 549
68.6%
ValueCountFrequency (%)
0.5 1
 
0.1%
1.1 1
 
0.1%
1.8 2
 
0.2%
1.9 1
 
0.1%
2 16
2.0%
2.1 11
1.4%
2.2 5
 
0.6%
2.3 6
 
0.8%
2.4 5
 
0.6%
2.5 6
 
0.8%
ValueCountFrequency (%)
38.9 1
 
0.1%
26.4 1
 
0.1%
24 2
0.2%
22 1
 
0.1%
20.8 3
0.4%
20 1
 
0.1%
14.5 1
 
0.1%
14.1 1
 
0.1%
14 1
 
0.1%
13.3 1
 
0.1%

Cr
Real number (ℝ)

HIGH CORRELATION 

Distinct104
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.55875
Minimum20
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-03-14T06:12:23.770287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile33
Q147
median59
Q373
95-th percentile106
Maximum800
Range780
Interquartile range (IQR)26

Descriptive statistics

Standard deviation57.591015
Coefficient of variation (CV)0.85245826
Kurtosis101.96186
Mean67.55875
Median Absolute Deviation (MAD)13
Skewness8.9283868
Sum54047
Variance3316.725
MonotonicityNot monotonic
2024-03-14T06:12:24.259329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 36
 
4.5%
55 25
 
3.1%
72 25
 
3.1%
48 23
 
2.9%
53 22
 
2.8%
46 21
 
2.6%
59 21
 
2.6%
70 21
 
2.6%
67 20
 
2.5%
45 18
 
2.2%
Other values (94) 568
71.0%
ValueCountFrequency (%)
20 2
 
0.2%
22 2
 
0.2%
23 3
 
0.4%
24 2
 
0.2%
26 1
 
0.1%
27 2
 
0.2%
28 4
0.5%
29 1
 
0.1%
30 6
0.8%
31 8
1.0%
ValueCountFrequency (%)
800 3
0.4%
401 2
0.2%
370 2
0.2%
315 2
0.2%
230 1
 
0.1%
228 1
 
0.1%
198 1
 
0.1%
194 1
 
0.1%
185 3
0.4%
179 1
 
0.1%

HbA1c
Real number (ℝ)

HIGH CORRELATION 

Distinct109
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.499325
Minimum0.9
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-03-14T06:12:24.748753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile4.3
Q16.8
median8.2
Q310.2
95-th percentile12.605
Maximum16
Range15.1
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation2.4684876
Coefficient of variation (CV)0.29043336
Kurtosis-0.25335251
Mean8.499325
Median Absolute Deviation (MAD)1.7
Skewness0.25242033
Sum6799.46
Variance6.0934308
MonotonicityNot monotonic
2024-03-14T06:12:25.181019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 32
 
4.0%
8 29
 
3.6%
6.8 28
 
3.5%
7 25
 
3.1%
4 22
 
2.8%
6 20
 
2.5%
10.2 19
 
2.4%
6.5 18
 
2.2%
7.2 18
 
2.2%
7.7 17
 
2.1%
Other values (99) 572
71.5%
ValueCountFrequency (%)
0.9 1
 
0.1%
3 1
 
0.1%
3.7 3
 
0.4%
4 22
2.8%
4.1 5
 
0.6%
4.2 3
 
0.4%
4.3 6
 
0.8%
4.5 4
 
0.5%
4.6 1
 
0.1%
4.7 1
 
0.1%
ValueCountFrequency (%)
16 1
 
0.1%
15.9 1
 
0.1%
15 2
0.2%
14.8 1
 
0.1%
14.7 3
0.4%
14.6 2
0.2%
14.5 2
0.2%
14.4 1
 
0.1%
14.1 1
 
0.1%
13.9 3
0.4%

Chol
Real number (ℝ)

Distinct74
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9559
Minimum0.6
Maximum10.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-03-14T06:12:25.481389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile3
Q14.1
median4.8
Q35.7
95-th percentile7.2
Maximum10.3
Range9.7
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.3113152
Coefficient of variation (CV)0.26459679
Kurtosis1.6763976
Mean4.9559
Median Absolute Deviation (MAD)0.8
Skewness0.74293476
Sum3964.72
Variance1.7195476
MonotonicityNot monotonic
2024-03-14T06:12:25.776540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4 37
 
4.6%
4 32
 
4.0%
5.3 32
 
4.0%
4.2 31
 
3.9%
4.8 30
 
3.8%
4.9 29
 
3.6%
4.1 28
 
3.5%
5.2 27
 
3.4%
5.4 25
 
3.1%
4.5 25
 
3.1%
Other values (64) 504
63.0%
ValueCountFrequency (%)
0.6 1
 
0.1%
1.2 1
 
0.1%
2 3
0.4%
2.1 1
 
0.1%
2.3 2
 
0.2%
2.4 4
0.5%
2.5 3
0.4%
2.6 4
0.5%
2.7 4
0.5%
2.8 5
0.6%
ValueCountFrequency (%)
10.3 1
 
0.1%
9.9 1
 
0.1%
9.8 2
0.2%
9.7 2
0.2%
9.5 3
0.4%
9.3 1
 
0.1%
9.2 1
 
0.1%
9.1 1
 
0.1%
8.8 2
0.2%
8.6 1
 
0.1%

TG
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3313875
Minimum0.3
Maximum13.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-03-14T06:12:26.066901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.8
Q11.5
median2
Q32.9
95-th percentile4.9
Maximum13.8
Range13.5
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.3971874
Coefficient of variation (CV)0.59929437
Kurtosis12.407036
Mean2.3313875
Median Absolute Deviation (MAD)0.7
Skewness2.4944046
Sum1865.11
Variance1.9521326
MonotonicityNot monotonic
2024-03-14T06:12:26.384706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 48
 
6.0%
2.1 46
 
5.8%
1.5 42
 
5.2%
1.3 38
 
4.8%
1.7 34
 
4.2%
1.9 32
 
4.0%
1.6 30
 
3.8%
1.8 28
 
3.5%
2.2 28
 
3.5%
1.2 25
 
3.1%
Other values (57) 449
56.1%
ValueCountFrequency (%)
0.3 2
 
0.2%
0.5 1
 
0.1%
0.6 11
1.4%
0.7 23
2.9%
0.8 16
2.0%
0.9 12
1.5%
1 24
3.0%
1.1 25
3.1%
1.19 3
 
0.4%
1.2 25
3.1%
ValueCountFrequency (%)
13.8 1
 
0.1%
12.7 1
 
0.1%
11.6 1
 
0.1%
8.5 1
 
0.1%
7.7 2
0.2%
7.2 1
 
0.1%
7 2
0.2%
6.8 3
0.4%
6.7 1
 
0.1%
6.3 1
 
0.1%

HDL
Real number (ℝ)

Distinct47
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2245625
Minimum0.2
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-03-14T06:12:26.713303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.7
Q10.9
median1.1
Q31.35
95-th percentile1.9525
Maximum9.9
Range9.7
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.70675256
Coefficient of variation (CV)0.57714699
Kurtosis58.511274
Mean1.2245625
Median Absolute Deviation (MAD)0.2
Skewness6.2197854
Sum979.65
Variance0.49949918
MonotonicityNot monotonic
2024-03-14T06:12:27.055513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 113
14.1%
1.1 112
14.0%
0.9 109
13.6%
0.8 61
7.6%
1.3 60
7.5%
1.2 57
7.1%
0.7 49
 
6.1%
1.4 43
 
5.4%
1.6 35
 
4.4%
1.8 28
 
3.5%
Other values (37) 133
16.6%
ValueCountFrequency (%)
0.2 1
 
0.1%
0.4 5
 
0.6%
0.5 5
 
0.6%
0.6 18
 
2.2%
0.7 49
6.1%
0.75 3
 
0.4%
0.8 61
7.6%
0.9 109
13.6%
0.95 1
 
0.1%
1 113
14.1%
ValueCountFrequency (%)
9.9 1
 
0.1%
9 1
 
0.1%
6.6 2
0.2%
6.3 1
 
0.1%
5 1
 
0.1%
3.9 1
 
0.1%
3.8 1
 
0.1%
3.6 2
0.2%
3.4 1
 
0.1%
3.2 3
0.4%

LDL
Real number (ℝ)

Distinct65
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.62105
Minimum0.3
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-03-14T06:12:27.450485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile1
Q11.7
median2.5
Q33.3
95-th percentile4.4
Maximum9.9
Range9.6
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.1673421
Coefficient of variation (CV)0.44537192
Kurtosis4.3292625
Mean2.62105
Median Absolute Deviation (MAD)0.8
Skewness1.2207339
Sum2096.84
Variance1.3626875
MonotonicityNot monotonic
2024-03-14T06:12:27.925848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 50
 
6.2%
2 44
 
5.5%
1.7 32
 
4.0%
3.1 30
 
3.8%
2.6 30
 
3.8%
1.4 29
 
3.6%
3.5 28
 
3.5%
3 27
 
3.4%
3.6 25
 
3.1%
4.1 24
 
3.0%
Other values (55) 481
60.1%
ValueCountFrequency (%)
0.3 1
 
0.1%
0.5 2
 
0.2%
0.6 2
 
0.2%
0.7 2
 
0.2%
0.75 2
 
0.2%
0.8 5
 
0.6%
0.9 19
2.4%
0.95 4
 
0.5%
0.96 1
 
0.1%
1 9
1.1%
ValueCountFrequency (%)
9.9 2
0.2%
7.9 2
0.2%
7.5 1
 
0.1%
7 1
 
0.1%
6.4 1
 
0.1%
5.9 1
 
0.1%
5.6 3
0.4%
5.5 4
0.5%
5.3 1
 
0.1%
5.1 1
 
0.1%

VLDL
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.93475
Minimum0.1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-03-14T06:12:28.641136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median1
Q31.5
95-th percentile9.025
Maximum35
Range34.9
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9308723
Coefficient of variation (CV)2.0317211
Kurtosis30.57851
Mean1.93475
Median Absolute Deviation (MAD)0.4
Skewness5.1775826
Sum1547.8
Variance15.451757
MonotonicityNot monotonic
2024-03-14T06:12:29.425587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 89
 
11.1%
0.7 75
 
9.4%
1 61
 
7.6%
0.6 61
 
7.6%
0.8 58
 
7.2%
1.5 56
 
7.0%
0.5 47
 
5.9%
1.1 42
 
5.2%
1.3 35
 
4.4%
2 33
 
4.1%
Other values (49) 243
30.4%
ValueCountFrequency (%)
0.1 2
 
0.2%
0.2 5
 
0.6%
0.3 23
 
2.9%
0.4 32
 
4.0%
0.5 47
5.9%
0.6 61
7.6%
0.7 75
9.4%
0.8 58
7.2%
0.9 89
11.1%
1 61
7.6%
ValueCountFrequency (%)
35 1
0.1%
33.6 1
0.1%
31.8 2
0.2%
31 1
0.1%
27.2 1
0.1%
24.5 1
0.1%
22.7 1
0.1%
22.2 2
0.2%
19.5 1
0.1%
18.1 1
0.1%

BMI
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.923062
Minimum19
Maximum47.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-03-14T06:12:29.967100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q126
median30
Q333
95-th percentile38
Maximum47.75
Range28.75
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.8771354
Coefficient of variation (CV)0.16298918
Kurtosis-0.30974642
Mean29.923062
Median Absolute Deviation (MAD)3
Skewness0.098361989
Sum23938.45
Variance23.78645
MonotonicityNot monotonic
2024-03-14T06:12:30.345839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 95
 
11.9%
33 84
 
10.5%
29 57
 
7.1%
26 55
 
6.9%
24 42
 
5.2%
28 42
 
5.2%
31 41
 
5.1%
27 39
 
4.9%
32 37
 
4.6%
37 30
 
3.8%
Other values (51) 278
34.8%
ValueCountFrequency (%)
19 4
 
0.5%
20 6
 
0.8%
21 25
3.1%
21.17 1
 
0.1%
22 23
2.9%
23 26
3.2%
23.5 1
 
0.1%
24 42
5.2%
24.6 1
 
0.1%
25 21
2.6%
ValueCountFrequency (%)
47.75 1
 
0.1%
47 1
 
0.1%
43.25 2
 
0.2%
40.5 2
 
0.2%
40 1
 
0.1%
39.18 2
 
0.2%
39 20
2.5%
38.62 1
 
0.1%
38 20
2.5%
37.62 1
 
0.1%

CLASS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
3
707 
1
 
58
2
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters800
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 707
88.4%
1 58
 
7.2%
2 35
 
4.4%

Length

2024-03-14T06:12:30.607647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T06:12:30.883521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 707
88.4%
1 58
 
7.2%
2 35
 
4.4%

Most occurring characters

ValueCountFrequency (%)
3 707
88.4%
1 58
 
7.2%
2 35
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 800
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 707
88.4%
1 58
 
7.2%
2 35
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 707
88.4%
1 58
 
7.2%
2 35
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 707
88.4%
1 58
 
7.2%
2 35
 
4.4%

Interactions

2024-03-14T06:12:16.052171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:49.149341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:51.711409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:55.144469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:58.492279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:02.404611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:04.835866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:07.375230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:10.167368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:13.299672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:16.341326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:49.403045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:52.001657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:55.409333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:58.815049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:02.636419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:05.087635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:07.643587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:10.462860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:13.558822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:16.638307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:49.668709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:52.351736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:55.679153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:59.209580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:02.881421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:05.366300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:07.896419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:10.824044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:13.830385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:16.961361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:49.967113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:52.919234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:55.946933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:59.484361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:03.137620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:05.630476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:08.161321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:11.243726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:14.110497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:17.267692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:50.226368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:53.566816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:56.255088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:59.733155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:03.383569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:05.897833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:08.415586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:11.578193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:14.375466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:17.526297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:50.462390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:53.829001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:56.623775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:59.967079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:03.614381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:06.134799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:08.672146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:11.929979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:14.608214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:17.822998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:50.725707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:54.124358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:57.018153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:00.338713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:03.857607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:06.381052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:08.919199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:12.319721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:14.890136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:18.138182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:50.974273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:54.375586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:57.372039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:00.859613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:04.097074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:06.627557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:09.154349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:12.554069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:15.134769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:18.422307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:51.216611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:54.626636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:57.700703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:01.173359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:04.342611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:06.867093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:09.480621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:12.814166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:15.449969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:18.742419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:51.464641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:54.882417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:11:58.095346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:02.144570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:04.608638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:07.126509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:09.888949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:13.063558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-14T06:12:15.750679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-03-14T06:12:31.097813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AGEBMICLASSCholCrGenderHDLHbA1cLDLTGUreaVLDL
AGE1.0000.3600.3850.0290.0780.103-0.0520.384-0.0090.1750.1600.071
BMI0.3601.0000.528-0.0260.0750.1700.0720.353-0.1290.1040.0820.213
CLASS0.3850.5281.0000.1630.1060.0850.0200.5140.0050.2860.1600.280
Chol0.029-0.0260.1631.000-0.0100.1230.0970.1540.4200.391-0.0100.273
Cr0.0780.0750.106-0.0101.0000.093-0.069-0.0700.0540.0760.5700.089
Gender0.1030.1700.0850.1230.0931.000-0.112-0.0330.047-0.0100.2170.060
HDL-0.0520.0720.0200.097-0.069-0.1121.000-0.007-0.208-0.128-0.074-0.024
HbA1c0.3840.3530.5140.154-0.070-0.033-0.0071.000-0.0120.2510.0350.239
LDL-0.009-0.1290.0050.4200.0540.047-0.208-0.0121.0000.068-0.0510.015
TG0.1750.1040.2860.3910.076-0.010-0.1280.2510.0681.0000.0770.626
Urea0.1600.0820.160-0.0100.5700.217-0.0740.035-0.0510.0771.0000.020
VLDL0.0710.2130.2800.2730.0890.060-0.0240.2390.0150.6260.0201.000

Missing values

2024-03-14T06:12:19.292190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T06:12:20.135018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GenderAGEUreaCrHbA1cCholTGHDLLDLVLDLBMICLASS
0F504.7464.94.20.92.41.40.524.01
1M264.5624.93.71.41.12.10.623.01
2F504.7464.94.20.92.41.40.524.01
3F504.7464.94.20.92.41.40.524.01
4M337.1464.94.91.00.82.00.421.01
5F452.3244.02.91.01.01.50.421.01
6F502.0504.03.61.30.92.10.624.01
7M484.7474.02.90.80.91.60.424.01
8M432.6674.03.80.92.43.71.021.01
9F323.6284.03.82.02.43.81.024.01
GenderAGEUreaCrHbA1cCholTGHDLLDLVLDLBMICLASS
990F574.1709.35.33.31.01.41.329.03
991F554.13413.95.41.61.63.10.733.03
992M553.1398.55.02.51.92.90.727.03
993M283.5618.54.51.91.12.60.837.03
994M6910.31857.74.91.91.23.00.737.03
995M7111.0977.07.51.71.21.80.630.03
996M313.06012.34.12.20.72.415.437.23
997M307.1816.74.11.11.22.48.127.43
998M385.8596.75.32.01.62.914.040.53
999M545.0676.93.81.71.13.00.733.03

Duplicate rows

Most frequently occurring

GenderAGEUreaCrHbA1cCholTGHDLLDLVLDLBMICLASS# duplicates
3F484.0386.84.42.31.302.201.025.034
5F504.7464.94.20.92.401.400.524.013
62M554.6776.56.01.51.304.100.630.033
69M555.7766.85.51.50.704.100.729.033
0F305.7536.05.41.71.403.300.722.022
1F393.0386.44.71.31.103.100.622.022
2F443.0395.59.51.71.302.500.621.012
4F493.3446.05.61.90.751.350.823.022
6F512.1465.94.12.71.002.001.236.032
7F513.4317.04.50.61.103.100.339.032